Chouakri Sba - Academia.edu (original) (raw)
Papers by Chouakri Sba
Computer Science & Information Technology ( CS & IT ), 2015
We present in this work an energy detection algorithm, based on spectral power estimation, in the... more We present in this work an energy detection algorithm, based on spectral power estimation, in the context of cognitive radio. The algorithm is based on the Neyman-Pearson test where the robustness of the appropriate spectral bands identification, is based, at one hand, on the 'judicious' choice of the probability of detection (P D) and false alarm probability (P F). First, we accomplish a comparative study between two techniques for estimation of PSD (Power Spectral Density): the periodogram and Welch methods. Also, the interest is focused on the choice of the optimal duration of observation where we can state that this latter one should be inversely proportional to the level of the SNR of the transmitted signal to be sensed. The developed algorithm is applied in the context of cognitive radio. The algorithm aims to identify the free spectral bands representing, reserved for the primary user, of the signal carrying information, issued from an ASCII encoding alphanumeric message and utilizing the BPSK modulation, transmitted through an AWGN (Added White Gaussian Noise) channel. The algorithm succeeds in identifying the free spectral bands even for low SNR levels (e.g. to-2 dB) and allocate them to the informative signal representing the secondary user.
The GSM (Global System for Mobile communications) Network uses GMSK modulation. This work will fo... more The GSM (Global System for Mobile communications) Network uses GMSK modulation. This work will focus on the influence of GMSK modulation parameters on the quality of the ECG signal transmitted via GSM network by simulation upon MATLAB/SIMULINK software. The synthesized work is based on setting parameters of GMSK modulation: the product BT, the power of the input signal, and the signal to noise ratio (SNR). To assess the obtained results we perform both quantitative (based on numerical computations) and qualitative (based on comparing, par visual perception, the transmitted and received ECG signal via GSM network and eye diagram) evaluations The obtained results show that for a minimum value of BER, for an arbitrary SNR value, it is recommended to use low input power signal to transmit ECG signal. Furthermore, the product ‘BT’ of value 0.3 represents a good compromise in sense of reducing the inter-symbol interference (ISI) and spectral efficiency. The aim of this work is to make a d...
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies - ISABEL '11, 2011
ABSTRACT An algorithm of the ECG signal compression, based on the combination of the run length e... more ABSTRACT An algorithm of the ECG signal compression, based on the combination of the run length encoding and discrete wavelet transform, intended for a simulated transmission via the IEEE 802.11b WLAN channel, is presented in this work. The algorithm consists of two basic phases that are ECG signal compression and transmission via the IEEE 802.11b WLAN channel. The algorithm is based on applying the run length coding upon the thresholded discrete wavelet transform of the real ECG signal. In terms of compression efficiency, applying the compression procedure on several ECG data, presenting diverse cardiac status, selected from the MIT-BIH arrhythmia data base, achieves compression ratio of around 10:1, normalized root mean squared error (NRMSE) of 4% and (mean± standard deviation) of the difference between the restituted ECG signal and the original one of around (3 10-6) ± 0.03. The end point of this work is to simulate transmission of the compressed ECG signal via the IEEE 802.11b WLAN channel. The unavoidable distortion introduced by the transmission channel reduces the compression ratio to about 6.7:1 in the cost of preserving the ECG signal fidelity.
Journal of Physics: Conference Series, 2013
ABSTRACT We present in this work an algorithm for electrocardiogram (ECG) signal compression aime... more ABSTRACT We present in this work an algorithm for electrocardiogram (ECG) signal compression aimed to its transmission via telecommunication channel. Basically, the proposed ECG compression algorithm is articulated on the use of wavelet transform, leading to low/high frequency components separation, high order statistics based thresholding, using level adjusted kurtosis value, to denoise the ECG signal, and next a linear predictive coding filter is applied to the wavelet coefficients producing a lower variance signal. This latter one will be coded using the Huffman encoding yielding an optimal coding length in terms of average value of bits per sample. At the receiver end point, with the assumption of an ideal communication channel, the inverse processes are carried out namely the Huffman decoding, inverse linear predictive coding filter and inverse discrete wavelet transform leading to the estimated version of the ECG signal. The proposed ECG compression algorithm is tested upon a set of ECG records extracted from the MIT-BIH Arrhythmia Data Base including different cardiac anomalies as well as the normal ECG signal. The obtained results are evaluated in terms of compression ratio and mean square error which are, respectively, around 1:8 and 7%. Besides the numerical evaluation, the visual perception demonstrates the high quality of ECG signal restitution where the different ECG waves are recovered correctly.
Journal of Physics Conference Series
We present in this work an algorithm for electrocardiogram (ECG) signal compression aimed to its ... more We present in this work an algorithm for electrocardiogram (ECG) signal compression aimed to its transmission via telecommunication channel. Basically, the proposed ECG compression algorithm is articulated on the use of wavelet transform, leading to low/high frequency components separation, high order statistics based thresholding, using level adjusted kurtosis value, to denoise the ECG signal, and next a linear predictive coding filter is applied to the wavelet coefficients producing a lower variance signal. This latter one will be coded using the Huffman encoding yielding an optimal coding length in terms of average value of bits per sample. At the receiver end point, with the assumption of an ideal communication channel, the inverse processes are carried out namely the Huffman decoding, inverse linear predictive coding filter and inverse discrete wavelet transform leading to the estimated version of the ECG signal. The proposed ECG compression algorithm is tested upon a set of EC...
We present in this work the efficiency of the wavelet transform, in exploring non-stationary sign... more We present in this work the efficiency of the wavelet transform, in exploring non-stationary signals such as those containing transients and discontinuities, and time varying spectra signals. We examine the nonstationarity problem as well as the alternative solutions suggested to deal with like the time-frequency analysis. In this context, we have applied the continuous wavelet transform-CWT-to a set of classical types of signals showing each a particular feature and to a real signal, which is the electrocardiogram ECG signal to evaluate the CWT efficiency. The obtained results demonstrated the higher ability of the wavelet transform in localizing specified temporal and spectral features of a signal.
Computers in cardiology
We present in this paper an algorithm of filtering the noisy real ECG signal. The classical wavel... more We present in this paper an algorithm of filtering the noisy real ECG signal. The classical wavelet denoising process, based on the Donoho et al. algorithm, at the 4 th level, appears clearly the P and T waves whereas the R waves undergo considerable distortion. This is due to the interference of the WGN and the free noise ECG detail sequences at level 4. To overcome this drawback, our key idea is to estimate the corrupted WGN and consequently remove the noise interfering R waves at the 4 th level detail sequence. Our denoising algorithm was applied to a set of the MIT-BIH Arrhythmia Database ECG records corrupted with a 0 dB WGN which provided an output SNR of around 6 dB and an MSE value of around 0.0011. A comparative analysis using the low pass Butterworth filter and the 4 th level classical wavelet denoising provides the output SNR values of around 3 dB and MSE value of around 0.0018; which demonstrates the superior performance of our proposed denoising algorithm.
We present in this work an energy detection algorithm, based on spectral power estimation, in the... more We present in this work an energy detection algorithm, based on spectral power estimation, in the context of cognitive radio. The algorithm is based on the Neyman-Pearson test where the robustness of the appropriate spectral bands identification, is based, at one hand, on the ‘judicious’ choice of the probability of detection (PD) and false alarm probability (PF). First, we accomplish a comparative study between two techniques for estimation of PSD (Power Spectral Density): the periodogram and Welch methods. Also, the interest is focused on the choice of the optimal duration of observation where we can state that this latter one should be inversely proportional to the level of the SNR of the transmitted signal to be sensed. The developed algorithm is applied in the context of cognitive radio. The algorithm aims to identify the free spectral bands representing, reserved for the primary user, of the signal carrying information, issued from an ASCII encoding alphanumeric message and utilizing the BPSK modulation, transmitted through an AWGN (Added White Gaussian Noise) channel. The algorithm succeeds in identifying the free spectral bands even for low SNR levels (e.g. to -2 dB) and allocate them to the informative signal representing the secondary user.
Computer Science & Information Technology ( CS & IT ), 2015
We present in this work an energy detection algorithm, based on spectral power estimation, in the... more We present in this work an energy detection algorithm, based on spectral power estimation, in the context of cognitive radio. The algorithm is based on the Neyman-Pearson test where the robustness of the appropriate spectral bands identification, is based, at one hand, on the 'judicious' choice of the probability of detection (P D) and false alarm probability (P F). First, we accomplish a comparative study between two techniques for estimation of PSD (Power Spectral Density): the periodogram and Welch methods. Also, the interest is focused on the choice of the optimal duration of observation where we can state that this latter one should be inversely proportional to the level of the SNR of the transmitted signal to be sensed. The developed algorithm is applied in the context of cognitive radio. The algorithm aims to identify the free spectral bands representing, reserved for the primary user, of the signal carrying information, issued from an ASCII encoding alphanumeric message and utilizing the BPSK modulation, transmitted through an AWGN (Added White Gaussian Noise) channel. The algorithm succeeds in identifying the free spectral bands even for low SNR levels (e.g. to-2 dB) and allocate them to the informative signal representing the secondary user.
The GSM (Global System for Mobile communications) Network uses GMSK modulation. This work will fo... more The GSM (Global System for Mobile communications) Network uses GMSK modulation. This work will focus on the influence of GMSK modulation parameters on the quality of the ECG signal transmitted via GSM network by simulation upon MATLAB/SIMULINK software. The synthesized work is based on setting parameters of GMSK modulation: the product BT, the power of the input signal, and the signal to noise ratio (SNR). To assess the obtained results we perform both quantitative (based on numerical computations) and qualitative (based on comparing, par visual perception, the transmitted and received ECG signal via GSM network and eye diagram) evaluations The obtained results show that for a minimum value of BER, for an arbitrary SNR value, it is recommended to use low input power signal to transmit ECG signal. Furthermore, the product ‘BT’ of value 0.3 represents a good compromise in sense of reducing the inter-symbol interference (ISI) and spectral efficiency. The aim of this work is to make a d...
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies - ISABEL '11, 2011
ABSTRACT An algorithm of the ECG signal compression, based on the combination of the run length e... more ABSTRACT An algorithm of the ECG signal compression, based on the combination of the run length encoding and discrete wavelet transform, intended for a simulated transmission via the IEEE 802.11b WLAN channel, is presented in this work. The algorithm consists of two basic phases that are ECG signal compression and transmission via the IEEE 802.11b WLAN channel. The algorithm is based on applying the run length coding upon the thresholded discrete wavelet transform of the real ECG signal. In terms of compression efficiency, applying the compression procedure on several ECG data, presenting diverse cardiac status, selected from the MIT-BIH arrhythmia data base, achieves compression ratio of around 10:1, normalized root mean squared error (NRMSE) of 4% and (mean± standard deviation) of the difference between the restituted ECG signal and the original one of around (3 10-6) ± 0.03. The end point of this work is to simulate transmission of the compressed ECG signal via the IEEE 802.11b WLAN channel. The unavoidable distortion introduced by the transmission channel reduces the compression ratio to about 6.7:1 in the cost of preserving the ECG signal fidelity.
Journal of Physics: Conference Series, 2013
ABSTRACT We present in this work an algorithm for electrocardiogram (ECG) signal compression aime... more ABSTRACT We present in this work an algorithm for electrocardiogram (ECG) signal compression aimed to its transmission via telecommunication channel. Basically, the proposed ECG compression algorithm is articulated on the use of wavelet transform, leading to low/high frequency components separation, high order statistics based thresholding, using level adjusted kurtosis value, to denoise the ECG signal, and next a linear predictive coding filter is applied to the wavelet coefficients producing a lower variance signal. This latter one will be coded using the Huffman encoding yielding an optimal coding length in terms of average value of bits per sample. At the receiver end point, with the assumption of an ideal communication channel, the inverse processes are carried out namely the Huffman decoding, inverse linear predictive coding filter and inverse discrete wavelet transform leading to the estimated version of the ECG signal. The proposed ECG compression algorithm is tested upon a set of ECG records extracted from the MIT-BIH Arrhythmia Data Base including different cardiac anomalies as well as the normal ECG signal. The obtained results are evaluated in terms of compression ratio and mean square error which are, respectively, around 1:8 and 7%. Besides the numerical evaluation, the visual perception demonstrates the high quality of ECG signal restitution where the different ECG waves are recovered correctly.
Journal of Physics Conference Series
We present in this work an algorithm for electrocardiogram (ECG) signal compression aimed to its ... more We present in this work an algorithm for electrocardiogram (ECG) signal compression aimed to its transmission via telecommunication channel. Basically, the proposed ECG compression algorithm is articulated on the use of wavelet transform, leading to low/high frequency components separation, high order statistics based thresholding, using level adjusted kurtosis value, to denoise the ECG signal, and next a linear predictive coding filter is applied to the wavelet coefficients producing a lower variance signal. This latter one will be coded using the Huffman encoding yielding an optimal coding length in terms of average value of bits per sample. At the receiver end point, with the assumption of an ideal communication channel, the inverse processes are carried out namely the Huffman decoding, inverse linear predictive coding filter and inverse discrete wavelet transform leading to the estimated version of the ECG signal. The proposed ECG compression algorithm is tested upon a set of EC...
We present in this work the efficiency of the wavelet transform, in exploring non-stationary sign... more We present in this work the efficiency of the wavelet transform, in exploring non-stationary signals such as those containing transients and discontinuities, and time varying spectra signals. We examine the nonstationarity problem as well as the alternative solutions suggested to deal with like the time-frequency analysis. In this context, we have applied the continuous wavelet transform-CWT-to a set of classical types of signals showing each a particular feature and to a real signal, which is the electrocardiogram ECG signal to evaluate the CWT efficiency. The obtained results demonstrated the higher ability of the wavelet transform in localizing specified temporal and spectral features of a signal.
Computers in cardiology
We present in this paper an algorithm of filtering the noisy real ECG signal. The classical wavel... more We present in this paper an algorithm of filtering the noisy real ECG signal. The classical wavelet denoising process, based on the Donoho et al. algorithm, at the 4 th level, appears clearly the P and T waves whereas the R waves undergo considerable distortion. This is due to the interference of the WGN and the free noise ECG detail sequences at level 4. To overcome this drawback, our key idea is to estimate the corrupted WGN and consequently remove the noise interfering R waves at the 4 th level detail sequence. Our denoising algorithm was applied to a set of the MIT-BIH Arrhythmia Database ECG records corrupted with a 0 dB WGN which provided an output SNR of around 6 dB and an MSE value of around 0.0011. A comparative analysis using the low pass Butterworth filter and the 4 th level classical wavelet denoising provides the output SNR values of around 3 dB and MSE value of around 0.0018; which demonstrates the superior performance of our proposed denoising algorithm.
We present in this work an energy detection algorithm, based on spectral power estimation, in the... more We present in this work an energy detection algorithm, based on spectral power estimation, in the context of cognitive radio. The algorithm is based on the Neyman-Pearson test where the robustness of the appropriate spectral bands identification, is based, at one hand, on the ‘judicious’ choice of the probability of detection (PD) and false alarm probability (PF). First, we accomplish a comparative study between two techniques for estimation of PSD (Power Spectral Density): the periodogram and Welch methods. Also, the interest is focused on the choice of the optimal duration of observation where we can state that this latter one should be inversely proportional to the level of the SNR of the transmitted signal to be sensed. The developed algorithm is applied in the context of cognitive radio. The algorithm aims to identify the free spectral bands representing, reserved for the primary user, of the signal carrying information, issued from an ASCII encoding alphanumeric message and utilizing the BPSK modulation, transmitted through an AWGN (Added White Gaussian Noise) channel. The algorithm succeeds in identifying the free spectral bands even for low SNR levels (e.g. to -2 dB) and allocate them to the informative signal representing the secondary user.